What is Evolutionary Operations Methodology (EVOP)
Evolutionary operations methodology are also referred as EVOP is a design of experiment (DOE) statistical method to improve the process or product quality. This process was first developed by Geroge Box in 1957.
The basic purpose of the evolutionary operations methodology (EVOP) is to improve a process through systematic changes in the operating conditions of a given set of factors. An experimental design is established and conducted through a series of phases and cycles. The effects are tested for statistical significance against experimental error when such error can be calculated.
When a factor is found to be significant, the operating conditions for that factor are reset and the experiment conducted again. This process continues until no further gain is achieved. Hence, the concept of an evolution is established.
The basic two components in Evolutionary Operations
- Favorable variants selection
When Would you Use Evolutionary Operations Methodology (EVOP)?
Evolutionary Operation (EVOP) is a best DOE process for manufacturing related operations. This process allows continuous improvement and entails small incremental changes in parameters, so that little or no process scarp is generated.
Application of EVOP is especially suitable when
- The system had more number of product performance conditions
- The process had 2 to 3 process variables
- The performance of process changes over time
- Process calculations needs to be minimized
Static Operations and Evolutionary Operations
In a regular manufacturing environment the plant runs as per defined operational conditions. In other words executing the operations as per the “standard work”. As a matter of fact, the manufacturing facility always insists the operators to perform the operation under the predefined conditions which is called Static operation method.
The static operation method improves the productivity, but apart from productivity, monitoring of product physical properties is also equally important. These physical properties may go outside of specification limits if random deviations from the standard work are allowed.
With a proper planned Evolutionary operations, the regular production runs each of the variant and continually repeating the cycle. Since cycle of variants follows a simple pattern, the persistent repetition of process allows evidence concerning the yield and physical properties of the product.
Since very small incremental change in process parameters, Evolutionary operation does not required any special concessions or resources. In this way the regular production process generate not only the product but also the information required for further improvement.
Evolutionary Operations process steps
- Define process performance characteristics that needs to be improved
- Identify process variables whose small changes will lead the process improvement and record their current condition
- Plan the incremental changes steps (must be small steps hence it will not impact the production) for each identified process variable
- For each variable, mark initial set of values as corners of simplex (For two variables its’ a triangle and use tetrahedron for three variables)
- Perform one run at current condition and also perform two runs with small incremental changes of one or both of the process variables.
- Record the results and identify the least favorable result from the runs, in other words identify the least favorable corner
- Then, perform a new run from the least favorable corner (which is the reflection or mirror image of least favorable run)
- Now identify the new least favorable condition, this run will replace the existing one and also leads to another new run and this process will go on
- The process starts at initial operating condition and moves on till more favorable results achieved.
Example of Evolutionary Operations Methodology (EVOP)
During Improve phase of DMAIC Evolutionary Operations Methodology improves a process through systematic changes in the operating conditions of a given set of factors
Example: “ABC Chocolate” production unit reported nearly 21.4% rejection which leads more rework, affecting the delivery schedules, and also customer satisfaction level.
Team realized that process will improve, if the two important parameters air pressure of machine (in psi) and also belt speed (RPM) settings are adjusted.
- Identify process performance characteristics – For example Reduction of scrap and the current rejection rate is 21.4%
- Identify process variables whose small changes will lead the process improvement – Air pressure of machine (in psi) and belt speed (RPM)
- The initial air pressure is 120 psi and also belt speed is 40 rpm
- Plan the incremental changes steps for each identified process variables – An increase in 10 psi as well as 5 RPM might lead to improve the process and reduces the rejection
- For instance, the initial run is 120 psi and 40 rpm and 2nd and 3rd runs are 130 psi, 45 rpm and 140 psi, 50 rpm respectively and reported the rejection rate
The corresponding results are
- From the above picture and table it is clear that run 1 is the least favorable, in other words highest rejection rate, so initiate a new run reflection or mirror image of the run 1 i.e new run 4
- From the above picture and table it is clear that run 3 is least favorable, so run 5 mirror image of the run 3
Procedure to calculate the new run value
New run value = (good value of process variable 1+ good value of process variable 2 – value of least favorable process variable)
Example: To calculate the run 5 value (from 2,3,4)
- Run 5 Air pressure value = (value 4+value 2 -value 3)= (135+130-125) = 140
- Similarly, Run 5 Belt speed value = (value 4+value 2 -value 3) = (40+45-45) =40
- Similarly follow the process and run the experiment till expected rejection rate achieved